[ARTICLE] Dual-Sided Rotor Design for Performance Boost of Switched Synchronous Reluctance Motors in Electric Vehicles (Canadian Journal of Applied Physics Research / 2024)
Bibliographic record
Abstract
Increasing environmental awareness is pushing the design of electic motors to favor none rare-earth solutions (i.e., without permanent magnets), and one such example is the SyncRM 2 (or concentrated-coiled SRM2) being proposed for the electric hybrid automobile Toyota Yaris. Following on an already established line of research on this topic, this article proposes a new design that re-assigns most of the magnetic material in the stator to the rotor — resulting in the Dual-sided SyncRM (a variant of the SRM2). The detrimental effect (caused by the extra gap) of slightly reducing the aligned inductance is overwhelmingly outweighed by the beneficial effect of drastically reducing the unaligned inductance. Extensive back-to-back FEMM analysis was conducted, where the recomputed SRM2 matches previous research, providing confidence to the favorable predictions of the Dual-sided SyncRM. Both performances are compared, with the venue being available for download on an open-source database. A realistic photo-rendered three-dimensional model is displayed and also available. An important outcome is the Dual-sided SyncRM torque (and power) increased by 29% (with respect to the SRM2), achieving a saliency ratio of 10 and an efficiency boost to 91% (at the rated operational speed of 1200rpm).CAD link: https://figshare.com/articles/dataset/_CAD_Design_of_the_Novel_Dual-sided_Synchronous_Reluctance_Motor/24634062FEMM data link: https://figshare.com/articles/dataset/_FEMM_Magnetic_Modeling_of_the_Dual-sided_Synchronous_Reluctance_Motor/24634068Applied Physics Research Journal link: https://ccsenet.org/journal/index.php/apr/article/view/0/49825-------------------------------------------------------------------------For more public data, please visit my Figshare profile: https://figshare.com/authors/Luis_Teia/10811244
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".